It is useful, in a mining context, to have a knowledge of the distribution of different kinds of rock in the undisturbed earth. For example, it is useful to be able to identify the in-ground distribution of ore-containing rock, and distinguish the ore rock from surrounding rock types so that mine planners can best determine how to go about removing the ore from the ground. Identifying rock types is conventionally done manually by geologists using rock samples collected on site. This can be a dangerous, time intensive and expensive function, providing motivation to seek for an automated solution.
Developing a fully autonomous, remotely-operated open-pit mine is also a desirable goal in the mining industry for reducing risk to human life and increasing mining efficiency. One of the challenges encountered in autonomous mining is in building and maintaining representations of the in-ground geology to determine the quantity and quality of the minerals of interest. This is a motivation of the automated rock recognition system described herein, which aims to extract useful properties such as rock type and strength from blast hole drilling data, also called “measurement-while-drilling” (MWD) data. The rock recognition results are highly desired by the mining industry as they provide information that can be used in the optimization of the mine operations as well as mine planning and design. For instance, a rock boundary map can be important for blast hole pattern design as well as general strategic mine planning, and rock strength can be used to adjust the drilling parameters (e.g., rotation speed and penetration rate, etc.) as well as optimizing the explosives loading for blasting.
In the case of an open pit mine, the MWD data used for automated rock recognition are typically measurements collected from sensors on large drill rigs used for blast hole drilling in the mine. The MWD data are primarily used to control and monitor the drilling process, which may itself be performed autonomously.
Rock recognition essentially relates the MWD data, which is a reflection of the drill performance, in a meaningful way to the physical properties of the rocks being drilled. Under a supervised classification scheme, MWD data are first labelled by experienced geologists based on other geological data, from which a classifier is trained and then used to classify any new coming MWD data.
The idea of relating drilling measurements to the properties of rocks has been studied previously in an empirical or statistical way, such as in “The Concept of Specific Energy in Rock Drilling” by R Teale (International Journal of Rock Mechanics and Mining Sciences, 2:57-73, 1965). Machine learning techniques have also been applied to drilling data based rock recognition, including Neural Networks (NN), Conditional Random Field models and Gaussian Process classification. All these methods attempt to identify rock types solely on discretely distributed individual holes from the corresponding drill performance data. The supervised learning based rock recognition methods classify the rock types based on the model trained from the existing labelled datasets.
Supervised learning tries to find the causal connection between the input observations to the output labels by generating a function that maps inputs, which is the MWD data in this case, to the desired output which is the rock types. The difficulty of this learning task increases significantly if a clear causal relation between the input and the output does not obviously exist, which is the case in the present instance.
It will be understood that any reference herein does not constitute an admission as to the common general knowledge of a person skilled in the art.